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Portfolio Optimization: A Comparative Study

arXiv 2307.05048 ↗First posted 11 Jul 2023q-fin.PM cs.LGcited by 0

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Abstract (arXiv)

Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 11 Jul 2023.

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References · 17

  1. 2019A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processingcited by 23
  2. 2020Stock Price Prediction Using CNN and LSTM-Based Deep Learning Modelscited by 21
  3. 2020A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Modelscited by 18
  4. 2021Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Modelscited by 15
  5. 2021Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCHcited by 12
  6. 2020Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Modelscited by 11
  7. 2021Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Modelcited by 11
  8. 2021Stock Portfolio Optimization Using a Deep Learning LSTM Modelcited by 9
  9. 2021Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learningcited by 7
  10. 2022Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Modelcited by 6
  11. 2022Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Modelcited by 5
  12. 2022Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stockscited by 4
5 more
  1. 2022Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Modelcited by 4
  2. 2022Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Marketcited by 4
  3. 2022A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stockscited by 3
  4. 2022Balancing Profit, Risk, and Sustainability for Portfolio Managementcited by 2
  5. 2022Stock Volatility Prediction using Time Series and Deep Learning Approachcited by 2

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